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Evolutionary Design Using Rewriting Systems
Hýsek, Jiří ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
This work provides an introduction to the evolutionary algorithms and evolutionary design. It also describes disadvantages of direct encoding of a genotype to a phenotype and a method of nontrivial encoding which can solve these problems. We are particularly talking about the problems of the scalability of evolved solutions. We discuss a possible solution of described problem - a nontrivial genotype-phenotype mapping called development. This technique is demonstrated on an evolutionary design of a sequence of rewriting rules which is able to construct arbitrarily large sorting networks.
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Sorting Networks Design Using Coevolutionary CGP
Fábry, Marko ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. Sorting networks are abstract models capable of sorting lists of numbers. Advantage of sorting networks is that they are easily implemented in hardware, but their design is very complex. One of the unconventional and effective ways to design sorting networks is Cartesian Genetic Programming (CGP). CGP is one of evolutionary algorithms that are inspired by Darwinian theory of evolution. Efficiency of the CGP algorithm can be increased by using coevolution. Coevolution is an approach that works with multiple populations, which are influencing one another and constantly evolving, thus prevent the local optima deadlock. In this work it is shown, that with the use of coevolution, it is possible to achieve nearly twice the acceleration compared to evolutionary design.
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Sorting Networks Design Using Coevolutionary CGP
Fábry, Marko ; Hrbáček, Radek (referee) ; Drahošová, Michaela (advisor)
This paper deals with sorting networks design using Cartesian Genetic Programming and coevolution. Sorting networks are abstract models capable of sorting lists of numbers. Advantage of sorting networks is that they are easily implemented in hardware, but their design is very complex. One of the unconventional and effective ways to design sorting networks is Cartesian Genetic Programming (CGP). CGP is one of evolutionary algorithms that are inspired by Darwinian theory of evolution. Efficiency of the CGP algorithm can be increased by using coevolution. Coevolution is an approach that works with multiple populations, which are influencing one another and constantly evolving, thus prevent the local optima deadlock. In this work it is shown, that with the use of coevolution, it is possible to achieve nearly twice the acceleration compared to evolutionary design.
|
|
|
Evolutionary Design Using Rewriting Systems
Hýsek, Jiří ; Jaroš, Jiří (referee) ; Bidlo, Michal (advisor)
This work provides an introduction to the evolutionary algorithms and evolutionary design. It also describes disadvantages of direct encoding of a genotype to a phenotype and a method of nontrivial encoding which can solve these problems. We are particularly talking about the problems of the scalability of evolved solutions. We discuss a possible solution of described problem - a nontrivial genotype-phenotype mapping called development. This technique is demonstrated on an evolutionary design of a sequence of rewriting rules which is able to construct arbitrarily large sorting networks.
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